from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		lowerCAmelCase_     =   ['image_processor', 'tokenizer']
		lowerCAmelCase_     =   'BlipImageProcessor'
		lowerCAmelCase_     =   ('BertTokenizer', 'BertTokenizerFast')
		def __init__(       self     :     List[str]     ,  snake_case__     :     int     ,  snake_case__     :     int		)      ->	Dict:
				_lowerCamelCase				   =  False
				super().__init__(snake_case__     ,  snake_case__		)
				_lowerCamelCase				   =  self.image_processor
		def __call__(       self     :     List[str]     ,  snake_case__     :     ImageInput = None     ,  snake_case__     :     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,  snake_case__     :     bool = True     ,  snake_case__     :     Union[bool, str, PaddingStrategy] = False     ,  snake_case__     :     Union[bool, str, TruncationStrategy] = None     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     int = 0     ,  snake_case__     :     Optional[int] = None     ,  snake_case__     :     Optional[bool] = None     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = False     ,  snake_case__     :     bool = True     ,  snake_case__     :     Optional[Union[str, TensorType]] = None     ,  **snake_case__     :     List[str]     ,  )      ->	BatchEncoding:
				if images is None and text is None:
						raise ValueError('You have to specify either images or text.'		)

				# Get only text
				if images is None:
						_lowerCamelCase				   =  self.tokenizer
						_lowerCamelCase				   =  self.tokenizer(
						    text=snake_case__     ,  add_special_tokens=snake_case__     ,  padding=snake_case__     ,  truncation=snake_case__     ,  max_length=snake_case__     ,  stride=snake_case__     ,  pad_to_multiple_of=snake_case__     ,  return_attention_mask=snake_case__     ,  return_overflowing_tokens=snake_case__     ,  return_special_tokens_mask=snake_case__     ,  return_offsets_mapping=snake_case__     ,  return_token_type_ids=snake_case__     ,  return_length=snake_case__     ,  verbose=snake_case__     ,  return_tensors=snake_case__     ,  **snake_case__     ,  )
						return text_encoding

				# add pixel_values
				_lowerCamelCase				   =  self.image_processor(snake_case__     ,  return_tensors=snake_case__		)

				if text is not None:
						_lowerCamelCase				   =  self.tokenizer(
						    text=snake_case__     ,  add_special_tokens=snake_case__     ,  padding=snake_case__     ,  truncation=snake_case__     ,  max_length=snake_case__     ,  stride=snake_case__     ,  pad_to_multiple_of=snake_case__     ,  return_attention_mask=snake_case__     ,  return_overflowing_tokens=snake_case__     ,  return_special_tokens_mask=snake_case__     ,  return_offsets_mapping=snake_case__     ,  return_token_type_ids=snake_case__     ,  return_length=snake_case__     ,  verbose=snake_case__     ,  return_tensors=snake_case__     ,  **snake_case__     ,  )
				else:
						_lowerCamelCase				   =  None

				if text_encoding is not None:
						encoding_image_processor.update(snake_case__		)

				return encoding_image_processor
		def   _snake_case       (       self     :     str     ,  *snake_case__     :     Tuple     ,  **snake_case__     :     Dict		)      ->	Any:
				return self.tokenizer.batch_decode(*snake_case__     ,  **snake_case__		)
		def   _snake_case       (       self     :     str     ,  *snake_case__     :     List[Any]     ,  **snake_case__     :     Optional[Any]		)      ->	Optional[int]:
				return self.tokenizer.decode(*snake_case__     ,  **snake_case__		)



		@property
		def   _snake_case       (       self     :     Union[str, Any]		)      ->	Tuple:
				_lowerCamelCase				   =  self.tokenizer.model_input_names
				_lowerCamelCase				   =  self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge





@total_ordering
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __lt__(       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				return self[-1] < other[-1]



		def __eq__(       self     :     Tuple     ,  snake_case__     :     Union[str, Any]		)      ->	Union[str, Any]:
				return self[-1] == other[-1]



def 	lowerCamelCase					(	UpperCamelCase     :				list							)   ->    list:
		_lowerCamelCase				   =  []
		# sort into stacks
		for element in collection:
				_lowerCamelCase				   =  Stack([element]							)
				_lowerCamelCase				   =  bisect_left(UpperCamelCase			,       UpperCamelCase							)
				if i != len(UpperCamelCase							):
						stacks[i].append(UpperCamelCase							)
				else:
						stacks.append(UpperCamelCase							)

    # use a heap-based merge to merge stack efficiently
		_lowerCamelCase				   =  merge(*(reversed(UpperCamelCase							) for stack in stacks)							)
		return collection


if __name__ == "__main__":
				A						=			input('Enter numbers separated by a comma:\n').strip()
				A						=			[int(item) for item in user_input.split(',')]
				print(patience_sort(unsorted))